Beyond objectivity: The performance impact of the perceived ability to learn and solve problems

Journal of Vocational Behavior 79 (2011) 484–495

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Journal of Vocational Behavior j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j v b

Beyond objectivity: The performance impact of the perceived ability to learn and solve problems Michael J. Tews a,⁎, John W. Michel b, Raymond A. Noe c a b c

School of Hospitality Management, 222 Mateer Building, The Pennsylvania State University, University Park, PA 16802, USA Department of Management, College of Business & Economics, Towson University, 8000 York Road, Towson, MD 21252, USA Fisher College of Business, Department of Management and Human Resources, 700 Fisher Hall, The Ohio State University, 2100 Neil Avenue, Columbus, OH 43210, USA

a r t i c l e

i n f o

Article history: Received 6 July 2010 Available online 9 November 2010 Keywords: Perceived ability Learning Problem solving Individual differences Performance

a b s t r a c t The purpose of this research was to develop and provide initial validation evidence for the performance impact of a measure of an individual's perceived ability to learn and solve problems (PALS). Building on the self-efficacy literature and the importance of learning and problem solving, the fundamental premise of this research was that PALS would significantly explain employee performance. In addition to demonstrating that PALS represented a distinct construct, PALS was a significant predictor of performance for managerial and entry-level employees in two different organizational contexts. Moreover, PALS explained additional variance in performance beyond general mental ability, personality, and similar constructs related to learning and problem solving. © 2010 Elsevier Inc. All rights reserved.

Now more than ever, learning and problem solving are critical for employee success on the job. Today's organizations must be agile and quickly adapt to changing customer demands for new and different products and services. As a result, employees need to creatively solve problems; deal with uncertain and unpredictable work situations; learn new tasks, technology, and procedures; and demonstrate adaptability (Pulakos, Arad, Donovan, & Plamondon, 2000). Molloy and Noe (2010) underscore this point by asserting that employees must “learn for a living.” That is, employees must constantly refine and add to their skill sets throughout their careers. Given the importance of learning and problem solving for employee performance, it is important to examine how individual differences related to these processes impact on-the-job performance success. In the extant literature, one of the most consistent findings is that general mental ability (GMA) is a key predictor of employee job performance (Hunter, 1986; Hunter & Hunter, 1984; Ree & Earles, 1992). For example, Hunter and Hunter's (1984) metaanalysis estimated GMA performance validities of .58 for professional managerial jobs, .56 for highly complex technical jobs, .51 for moderately complex jobs, .40 for semi-skilled jobs, and .23 for completely unskilled jobs. Given these findings, Schmidt and Hunter (1998) argued that GMA should be the primary basis on which to select employees. GMA is important because learning and problem solving are central to performance success, and GMA is a primary individual difference that influences learning and problem solving (Gottfredson, 2002). Notwithstanding the importance and validity of GMA, additional constructs related to learning and problem solving may have value in predicting performance. In particular, an individual's perceptions of his or her ability to learn and solve problems may be of particular relevance. Such perceptions may motivate employees to engage in learning and problem-solving efforts, and therefore have a positive impact on employee performance. In their critical review of the staffing literature, Cascio and Aguinis (2008) argued that the current staffing model has reached a ceiling in its ability to predict performance. Thus, while GMA in combination with other predictors (such as personality) do in fact reliably predict performance, a significant proportion of variance is still yet unexplained, suggesting the need to examine additional predictors. ⁎ Corresponding author. E-mail addresses: [email protected] (M.J. Tews), [email protected] (J.W. Michel), [email protected] (R.A. Noe). 0001-8791/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.jvb.2010.11.005

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As one step forward toward addressing this need, this research focuses on the development and validation of a measure of an individual's perceived ability to learn and solve problems (PALS). We begin by discussing the conceptual foundation for PALS. We then report the results of three studies, which together provide initial construct validity evidence for PALS. Study 1 focuses on initial scale development. Studies 2 and 3 establish the discriminant and predictive validity of the PALS construct. This research contributes to the literature by demonstrating that PALS is a distinct construct and by showing that PALS explains unique variance in performance beyond other predictors including GMA, the Big Five personality dimensions, generalized self-efficacy, and learning orientation. 1. The perceived ability to learn and solve problems We define PALS as confidence in one's ability to effectively learn, acquire new knowledge and skills, and solve problems on the job. As highlighted above, we focus on learning and problem solving, given the importance of these processes in facilitating individual performance success. Schmidt (2002) articulates that higher levels of job knowledge lead to higher levels of performance. Moreover, beyond job knowledge itself, successful performance requires direct problem solving on the job. Whereas GMA reflects an individual's actual ability to learn and solve problems, PALS reflects an individual's confidence in doing so. The importance of confidence in one's abilities lies in vast body of research on self-efficacy. Banudra (1986, p. 391) defined selfefficacy as “peoples' judgments of their capabilities to organize and execute courses of action required to attain designated types of performances.” That is, self-efficacy reflects a person's confidence in his or her capabilities to perform a task. Wood and Bandura (1989, p. 408) contended that self-efficacy beliefs relate to individuals' perceived capabilities “to mobilize the motivation, cognitive resources, and courses of action to meet given situational demands.” They further articulated that self-efficacy has a positive influence on performance because these beliefs impact “the challenges that are undertaken, the amount of effort expended in an endeavor, the level of perseverance in the face of difficulties, whether thinking patterns take self-aiding or selfimpeding forms, and vulnerability to stress and depression” (p. 408). One way of conceptualizing self-efficacy is as a belief that one can succeed across contexts, which is known as generalized selfefficacy (GSE). Specifically, GSE refers to the extent to which a person has an enduring belief that he or she is capable of accomplishment irrespective of the situation or task demands (Chen, Gully, & Eden, 2001; Judge, Erez, & Bono, 1998; Judge, Locke, & Durham, 1997). In other words, GSE refers to one's overall confidence to succeed in various achievement situations. Judge and Bono's (2001) meta-analysis provides support for the relationship between GSE and performance by demonstrating a weighted average correlation of .23 between GSE and job performance across 81 studies. More commonly, self-efficacy has been conceptualized as an individual's confidence to succeed in a particular performance domain (Banudra, 1986), which is often referred to as task-specific self-efficacy (TSSE) (Gist & Mitchell, 1992; Stajkovic & Luthans, 1998). Bandura argued that self-efficacy beliefs are not necessarily constant across performance domains. Thus, according to Bandura, self-efficacy beliefs should be examined in a domain-specific context if the goal is to maximize performance in that particular domain. Stajkovic and Luthans's (1998) meta-analysis provides support for the relationship between TSSE and performance. Specifically, they demonstrated a weighted average correlation of .38 between TSSE and work-related performance across 114 studies. Consistent with Bandura's argument that TSSE is more relevant than GSE, Stajkovic and Luthans's average correlation is larger than that estimated in Judge and Bono's (2001) research. Interestingly, Judge, Jackson, Shaw, Scott, and Rich's (2007) meta-analysis on the impact of self-efficacy on work-related performance calls into question the importance of TSSE relative to other individual differences. Specifically, they demonstrated that after controlling for key individual differences – GMA, agreeableness, conscientiousness, emotional stability, extraversion, openness to experience, and previous experience – TSSE did not significantly predict performance. Furthermore, the inclusion of TSSE after the other individual differences did not result in a significant ΔR2 in the prediction of performance. Several of the other individual differences were, however, significantly related to TSSE (i.e., GMA, conscientiousness, emotional stability, extraversion, and previous experience) and to performance (i.e., GMA, conscientiousness, and previous experience). Thus, while Stajkovic and Luthans' (1998) findings demonstrate that TSSE does have a positive performance impact, Judge et al.'s findings suggest that TSSE may be less important once more distal individual differences are considered. In the context of employee job performance, PALS reflects a mid-range level of specificity between GSE and TSSE. While GSE relates to confidence across all achievement domains, PALS is a form of TSSE that is narrowly focused on learning problem solving. PALS is more general, however, than TSSE beliefs that are isomorphically linked to specific performance criteria. We would like to highlight that we are in agreement that TSSE beliefs that are narrowly linked to specific criteria may best predict performance in a narrowly defined context (Chen, Gully, & Eden, 2004; Kanfer & Heggestad, 1997). However, it is our contention that PALS will be valuable for predicting performance across a number of job contexts, given the importance of learning and problem solving in every job. Just as GMA generalizes in predicting performance by influencing one's ability to learn and solve problems across contexts, self-efficacy beliefs related to an individual's ability to learn and solve problems may also have a generalizable influence on performance. It should be emphasized that much research has focused on the role of self-efficacy related to learning in training, development, and other learning contexts. Colquitt, LePine, and Noe's (2000) meta-analysis on training motivation demonstrated that selfefficacy related to learning is important in formal training contexts in facilitating knowledge and skill acquisition. Following Colquitt et al.'s (2002) work, research has continued to focus on self-efficacy related to learning. For example, Lee and Klein (2002) demonstrated that self-efficacy related to academic performance was related to subsequent examination performance among a sample of MBA students. Maurer, Weiss, and Barbeite (2003) demonstrated that self-efficacy for development was related to

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participation in development activities among a large sample of adults. Moreover, Maurer, Wrenn, Pierce, Tross, and Collins (2003) found that self-efficacy for learning and development was related to more favorable learning-oriented attitudes among undergraduate students. The current research examines the relationship between PALS and job performance, rather than PALS in relation to specific learning and problem-solving tasks in a formal learning environment. Examining the PALS–job performance relationship is important because research suggests that the majority of learning and problem solving occurs informally on the job with the intent of improving job performance (Flynn, Eddy, & Tannenbaum, 2006; Tannenbaum, 1997). Informal learning occurs on an as needed basis involving knowledge and skill acquisition needed for effective performance. Informal learning encompasses intent to learn, experience and action, feedback, and self-reflection (Marsick & Watkins, 1999). Examining the PALS–job performance relationship is warranted because in today's workplace, job performance requires individuals to adjust or adapt to new conditions and unexpected job requirements (Pulakos et al., 2000). In addition to self-efficacy beliefs, PALS is thought to be related to, yet distinct from, two other constructs. The first of which is openness to experience, one of the Big Five personality dimensions. Openness to experience reflects characteristics such as imaginativeness, artistic sensitivity, curiosity, broad-mindedness, intelligence, and creativity (Barrick & Mount, 1991; Costa & McCrae, 1992). To an extent, these characteristics may reflect one's proclivity to learn and solve problems. For example, an individual who is curious, broad-minded, and intelligent may have a desire and the ability to learn new information. Similarly, an individual who is imaginative and creative may be better at solving complex problems. PALS is distinct from openness to experience, however, in that PALS focuses narrowly on competence related to learning and problem solving. Openness to experience is broader, representing “receptivity to many varieties of experience and a fluid and permeable structure of consciousness” (McCrae, 1994, p. 251). PALS is also argued to be related to, but distinct from, an individual's learning orientation. Kozlowski, Gully, Brown, Salas, Smith, and Nason (2001) characterize learning orientation as “an adaptive response to novel or challenging achievement situations” (p. 4). Individuals with a learning orientation are attracted to challenging situations and believe that effort directed toward exploration and learning will yield self-improvement. A learning orientation can be contrasted with a performance orientation, which reflects the extent to which individuals seek out “easy situations that ensure positive evaluations of their capabilities” (p. 4). People with a performance orientation prefer to gain favorable judgments of their competence, whereas people with a learning orientation prefer to focus on finding ways to improve their competence (Dweck & Leggett, 1988). Both PALS and learning orientation are similar in that they focus on learning and problem solving. PALS, however, reflects confidence regarding learning and problem solving, whereas learning orientation reflects individuals' goals in such situations. Just as self-efficacy and learning orientation are distinct constructs (Chen, Gully, Whiteman, & Kilcullen, 2000), PALS is argued to be distinct from learning orientation. In the following sections, we detail three studies that provide preliminary construct validity evidence for PALS. The first study describes the initial scale development efforts. The second study provides evidence for the discriminant and predictive validity of PALS relative to related constructs with a sample of managerial employees. The third study provides further evidence for the predictive validity of PALS with a sample of entry-level employees. 2. Study 1 The purpose of study 1 was to develop a measure to assess the PALS construct. We sought to develop a new measure to assess PALS because extant self-efficacy measures related to learning tend to be too context specific. For example, Martocchio's (1994) measure of self-efficacy used in research on computer-based training included items such as “Using microcomputers is probably something I will be good at.” Maurer and Tarulli's (1994) measure of self-efficacy for development, which was used in Maurer, Weiss, et al. (2003), included items such as “If I were to participate in a development activity (workshop, course, etc.), my success in that activity would be at least comparable to most others.” The self-efficacy measure used in Lee and Klein (2002) asked participants to indicate how confident they were of attaining different test scores. Notwithstanding the validity of these and similar scales, we sought to develop a measure of one's perceived ability to learn and solve problems that was more general and thus potentially applicable across a variety of contexts. An initial list of eight items was generated to operationalize the PALS construct. Items were based on the terms learning and problem solving, synonyms for these terms, and closely related processes. For example, the items reflect an individual's confidence to be trained, learn new things, retain information, find solutions to problems, and reason. Next, the first two authors reviewed the items based on rules outlined by Edwards, Thomas, Rosenfeld, and Booth-Kewley (1997). Specifically, the items were reviewed and refined to ensure that they were not (1) too ambiguous or vague or (2) double-barreled but were (3) appropriately worded and (4) simple and specific. Two reverse-coded items were removed from the scale (e.g., “I have difficultly learning new things”) because research has demonstrated that reverse-coded items may compromise a scale's factor structure (Williams, Ford, & Nguyen, 2002). The final six-scale items are presented in the Appendix. Following the development of the scale items, data were collected and analyzed to assess the factor structure of the measure. 2.1. Methods The sample for the factor analysis was 163 undergraduate students at a large, public university located in the Midwestern US. The respondents participated on a voluntary and anonymous basis. The average age was 22 years, 52% were males, and 88% were

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Caucasian. The respondents completed a survey during class time that assessed PALS, along with other items not used for research purposes. The students indicated the extent to which each of the six items from the PALS accurately described themselves on a 5-point scale with anchors ranging from (1) very inaccurate to (5) very accurate. A principle components analysis (PCA) with varimax rotation was performed (Schwab, 1980) to determine the factor structure for the measure. PCA is most appropriate in early stages of scale development because it does not impose an assumption that a hypothetical model underlies the data (Ford, MacCallum, & Tait, 1986; Kelloway, 1995). 2.2. Results In evaluating the results from the PCA, two decision rules were employed to determine the number of components in the model: (1) eigenvalues over 1.0 and (2) components that were discrete from other factors as indicated in the scree plot. This analysis yielded a single factor that accounted for 50.4% of the variance. In addition, the factor loadings for the individual items ranged from .50 to .82. The internal consistency reliability estimate for this scale was acceptable (α = .83). These results suggest that the PALS construct is best represented by a single factor. 3. Study 2 The first purpose of study 2 was to establish the discriminant validity of PALS. Namely, the purpose was to show that PALS is distinct from GSE, openness to experience, and learning orientation. Although both PALS and GSE assess confidence, PALS is specific to learning and problem solving. PALS is distinct from openness to experience because it is more narrowly focused on learning and problem solving, whereas openness reflects broader intellectual curiosity and creativity. Finally, compared to learning orientation, PALS reflects confidence in one's ability to learn and solve problems, whereas learning orientation refers specifically to individuals' goals in such situations. Hypothesis 1. PALS will be distinct from GSE, openness to experience, and learning orientation. The fundamental premise of this research is that PALS will have a positive impact on job performance. Thus, the second purpose of study 2 was to provide evidence for the predictive validity of PALS. The positive PALS–job performance relationship is expected for two reasons. First, the ability to learn and solve problems is critical for successful performance. Second, confidence in one's abilities directs an individual to try to perform effectively in a work context. Hypothesis 2. PALS will be positively related to job performance. Beyond examining the PALS–job performance relationship, we investigate the predictive validity of PALS relative to other related individual differences. Specifically, the predictive validity of PALS will be examined relative to GMA, GSE, openness to experience, and learning orientation. That is, this research will examine whether PALS is a better predictor of job performance than each of these individual differences. Research questions, as opposed to directional hypotheses, are presented since it is not altogether clear whether PALS will be a stronger predictor, given the limited research in this area. Question 1. Will PALS be a stronger predictor of performance than GMA, GSE, openness to experience, and learning orientation? 3.1. Methods Two hundred and sixty-five managers from a national restaurant chain served as the basis for study 2. The data on the individual differences were obtained through online survey administration, and the performance data were obtained from organizational records. The sample included managers from the approximately 110 restaurants company-wide. The 265 managers in the sample represented approximately 65% of the managers in the organization. Approximately 75% of the respondents were males, and approximately 75% were Caucasian. The average age was 39 years, and the average tenure was 6 years. Along with the PALS scale, the full scales for GSE, openness to experience, and learning orientation are presented in the Appendix. 3.1.1. PALS The six-item scale from study 1 was used to assess PALS. The internal consistency reliability estimate was again .83. 3.1.2. GSE GSE was measured with eight items from Judge, Locke, Durham, and Kluger (1998). A sample item included “I can handle the situations that life brings.” The five-point scale ranged from 1 = very inaccurate to 5 = very accurate. The internal consistency reliability estimate was .81. 3.1.3. Openness to experience Openness to experience was assessed with the four-item measure from the Mini-IPIP (Donnellan, Oswald, Baird & Lucas, 2006). A sample item included “I have a vivid imagination.” The five-point scale ranged from 1 = very inaccurate to 5 = very accurate. The

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internal consistency reliability estimate was .65. While the internal consistency estimate for this scale is relatively low, it is similar to that reported in Donnellan et al.'s (2006) validation study. 3.1.4. Learning orientation Learning orientation was measured with VandeWalle's (1997) five-item measure. A sample item included “I like challenging work assignments I can learn a lot from.” The five-point scale ranged from 1 = very inaccurate to 5 = very accurate. The internal consistency reliability estimate was .76. 3.1.5. GMA GMA was assessed with the Wonderlic QuickTest (WPT-Q) (Wonderlic, Inc., 2004a). This online, 8-min timed assessment includes a series of 30 verbal, numeric, and logic problems. An individual's resulting score reflects a projected score on the full Wonderlic Personnel Test, ranging from 0 to 50. It has been demonstrated that WPT-Q scores correlate .93 with scores from the full Wonderlic assessment (Wonderlic, Inc., 2004b). 3.1.6. Performance The measure of managerial performance was based on the twelve dimensions from the organization's performance appraisal. The items relate to goal achievement focused on sales, profitability, employee retention, health and safety, and customer satisfaction. A manager's superior rated him or her on each performance item with a five-point scale with anchors ranging from 1 = unsatisfactory to 5 = exceptional. The internal consistency reliability estimate for the measure was .77. 3.2. Results Table 1 provides the descriptive statistics and correlations among the study variables. To assess the discriminant validity of PALS, GSE, openness to experience, and learning orientation, confirmatory factor analysis (CFA) was performed using Mplus 5.21 (Muthen & Muthen, 2007) with the sample covariance matrix as input and a maximum likelihood solution. Although the model possessed a statistically significant Chi-squared statistic, χ2 (224, n = 265) = 404.61, p b .01, the individual fit indices provided adequate support for the four-factor model (Hu & Bentler, 1999). Specifically, the Comparative Fit Index (CFI) was .93; the Tucker-Lewis Index (TLI) was .92; the root–mean–square error of approximation (RMSEA) was .06 (90% confidence interval ranged from .05 to .06); and the standardized root–mean–square residual (SRMR) was .05. Because the constructs were highly correlated, discriminant validity was further established by comparing two models for every pair of latent variables. In one model, items were fit to their respective hypothesized latent variable (e.g., PALS or GSE), and in the second model, all items were fit to a single latent variable. Pairwise Chi-squared difference tests were conducted to ensure that the two-factor models fit the data better than each single-factor model. In each case, the two-factor models fit the data significantly better, providing additional evidence for the discriminant validity of these constructs (Bagozzi & Phillips, 1982). Overall, these results suggest that PALS is distinct from GSE, openness to experience, and learning orientation. Thus, Hypothesis 1 was supported. Table 2 refers to the following paragraph. To serve as the basis for assessing the predictive validity of PALS, performance was regressed on the independent variables in a two-stage hierarchical multiple regression. Performance was regressed on GMA, GSE, openness to experience, and learning orientation in stage 1, with the inclusion of PALS in stage 2. For the independent variables, the variance inflation factors (VIFs) ranged from 1.07 for GMA to 2.35 for PALS. Given that the VIFs were less than 10, substantial multicollinearity was not present, which might have otherwise biased the coefficients (Cohen, Cohen, West, & Aiken, 2003). The R2 was .04 and in stage 1 and .06 in stage 2. The ΔR2 of .02, although modest, was significant after the inclusion of PALS [F = 5.43 (p b .05)]. GMA was a significant predictor in stage 1 (β = .10, p b .05), but a non-significant predictor in stage 2 (β = .09, p N .05) (Table 2). Hypothesis 2, which proposed that PALS would be positively related to job performance, was supported. The beta was .22 for managerial performance (p b .05). The research question asked whether PALS would be a stronger predictor of performance than GMA, GSE, openness to experience, and learning orientation, respectively. PALS was deemed to be a stronger predictor of performance than all four of the other individual differences, as none of the four was a significant predictor in stage 2, whereas PALS was. As noted above, the beta for GMA in stage 2 was .09 (p N .05). Further, the beta for GSE was .05 (p N .05), −.04 for openness to experience (p N .05), and −.04

Table 1 Descriptive statistics and correlations among study variables for study 1.

1. 2. 3. 4. 5. 6.

Performance PALS GMA GSE Openness Learning orientation

M

SD

1

2

3.08 4.24 25.01 4.43 3.87 4.12

.39 .47 4.47 .45 .39 .48

– .21** .12* .15** .11* .11*

– .13* .66** .60** .65**

Note. n = 265. Significance levels reflect one-tailed tests. *p b .05, **p b .01.

3

4

5

6

– .53** .63**

– .57**

–

– .06 .16** −.03

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Table 2 Regression of performance on PALS, GMA, GSE, openness, and learning orientation. Stage 1

Stage 2

Predictor

β

β

GMA GSE Openness Learning orientation PALS R2 F ΔR2 FΔ

.10* .12 .01 .03 – .04 2.36* – –

.09 .05 −.04 −.04 .22* .06 3.00* .02 5.43*

Note. n = 265. Significance levels reflect one-tailed tests. *p b .05, **p b .01.

for learning orientation (p N .05). A non-significant beta coefficient indicates that an effect size is not significantly different than zero. Therefore, if one coefficient is significant and one is not, the significant coefficient is a stronger predictor. Had the GSE, openness to experience, and learning orientation coefficients been significant, follow-up tests to assess significant differences with PALS would have been conducted utilizing the formula outlined by Paternoster, Brame, Mazerolle, and Piquero (1998) for testing the equality of regression coefficients. 3.2.1. Post hoc analysis As detailed above, once PALS was included in the regression equation, the impact of GMA on performance became nonsignificant, suggesting that PALS might mediate the GMA–performance relationship. To fully examine this proposition, a mediating analysis was conducted using the bootstrap approach detailed by Preacher and Hayes (2004). This approach is similar to Baron and Kenny's (1986) approach in that regression coefficients and standard errors are provided for each step required for assessing mediating relationships (x → m, m → y controlling for x, x → y controlling for m) (Kenny, Kasher, & Bolger, 1998). However, unlike the Baron and Kenny approach, the bootstrap approach reduces Type I error, increases power, and does not require that normality assumptions be met for variables and sampling distributions (Mooney & Duval, 1993). Using k number of resamples, the bootstrap approach yields a sampling distribution of the indirect effect with confidence intervals. The bounds of the 95% confidence interval indicate whether the population indirect effect is significantly different from zero (Preacher & Hayes, 2004). MacKinnon, Lockwood, and Williams (2004) advocate the use of confidence intervals to interpret tests of indirect effects because confidence intervals attained from resampling (i.e., bootstrapping) techniques provide both an estimation of the size and variability of the indirect effect. The relationships between GMA and PALS (b = .01, p b .05) and PALS and performance after controlling for GMA (b = .16, p b .01) were both significant. Furthermore, after controlling for PALS, the relationship between GMA and performance was nonsignificant (b = .01, p N .05). However, the bootstrapped indirect effect (k = 5,000 resamples) was non-significant, as the 95% confidence interval generated from the sampling distribution included zero (LL = .00, UL = .01). The inclusion of zero indicates that the indirect effect in the population is not significantly different from zero, thus providing no evidence of mediation (Preacher & Hayes, 2004). Thus, the results do not provide support for PALS mediating the GMA–performance relationship. 4. Study 3 The purpose of study 3 was to assess the generalizability of the predictive validity of PALS in another employment context. For this study, the predictive validity of PALS was examined for entry-level service employees. While learning and problem solving may be more relevant in higher level positions, they are critical in entry-level positions as well. Schmidt (2002) contends that knowledge and skill requirements in entry-level jobs are much greater than typically realized. Thus, it is proposed that PALS will be a relevant predictor of performance for entry-level employees. Hypothesis 1. PALS will be positively related to job performance. Study 3 also examines the predictive validity of PALS relative to GMA and the complete set of the Big Five personality dimensions (agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience). Meta-analyses of the impact of the Big Five personality dimensions on job performance and training performance demonstrate the value of these attributes. Conscientiousness is the strongest predictor of performance across jobs (Hurtz & Donovan, 2000) and of motivation to learn in training (Colquitt et al., 2000). Emotional stability is significantly, although modestly, related to performance across jobs as well (Hurtz & Donovan, 2000). Each of the Big Five has been shown to be significantly related to performance for jobs involving interpersonal interactions, such as those in the present study (Mount, Barrick, & Stewart, 1998). Conscientiousness has the strongest influence, followed by agreeableness, emotional stability, extraversion, and openness to experience. The following research question will be answered.

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Question 1. Will PALS be a stronger predictor of performance than GMA and the Big Five personality dimensions? 4.1. Methods The sample for study 3 consisted of 133 servers from 17 restaurants in one region of a different national restaurant than that used in study 2. The research team visited each restaurant for 1 day and administered surveys to all servers scheduled to work during that time. To obtain the performance ratings for the servers, two managers were provided with performance evaluations for each participating employee. These evaluations were completed within 1 week's time, on average, and then returned directly to the researchers. The 133 employees represented approximately one-third of the total servers in the 17 restaurants. Sixty-seven percent of the respondents were female, and 90% were Caucasian. The average age was 25 years, and the average tenure was 25 months. 4.1.1. PALS The internal consistency reliability estimate for the six-item scale was .80. 4.1.2. GMA GMA was measured using the written Wonderlic Personnel Test, Form A (Wonderlic, 2001). The assessment consists of 50 items and was administered under the standard 12-min, timed protocol. 4.1.3. Personality The Big Five were measured with the Mini-IPIP (Donnellan et al., 2006). Four items were used to measure each dimension. Sample items included the following: “I sympathize with others' feelings” (agreeableness), “I like order” (conscientiousness), “I am relaxed most of the time” (emotional stability), “I am the life of the party” (extraversion), and “I have a vivid imagination” (openness to experience). The employees indicated the extent to which each statement generally described themselves with response choices ranging from 1 = very inaccurate to 5 = very accurate. The internal consistency estimates were .75, .73, .67, .70, and .63 for agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience, respectively. While the internal consistency estimates for emotional stability and openness to experience are relatively low, they are similar to those reported in Donnellan et al.'s (2006) validation study. 4.1.4. Performance The three-item measure of performance was based on Williams and Anderson's (1991) in-role performance scale. A sample item was “This employee performs assigned tasks efficiently.” For each employee, two managers indicated the extent to which each performance item generally described the employee with response choices ranging from 1 = strongly disagree to 5 = strongly agree. The overall performance score for each employee was first created based on each manager's ratings and then averaged across the two managers to yield a single score. The median rwg,, an estimate of interrater agreement (James, 1982), for the performance ratings was .80. The internal consistency reliability estimate for the measure was .86. 4.2. Results Table 3 provides the descriptive statistics and correlations among the study variables. Performance was regressed on the independent variables in a two-stage hierarchical multiple regression, similar to the analytic strategy used in study 2. Performance was regressed on GMA and the personality dimensions in the first stage and PALS entered in the second stage. The VIFs ranged from 1.15 for emotional stability to 1.68 for PALS. The R2 was .11 in stage 1 and .15 in stage 2. The ΔR2 of .04 was significant after the inclusion of PALS [F = 5.10 (p b .05)]. GMA and conscientiousness were both significant predictors in stage 1. The beta for GMA was .16 (p b .05), and the conscientiousness beta was .21 (p b .05). However, they were nonsignificant predictors in stage 2. With the inclusion of PALS, the beta for GMA was .12 (p N .05), and the conscientiousness beta was .11 (p N .05) (Table 4).

Table 3 Descriptive statistics and correlations among study variables for study 3.

1. 2. 3. 4. 5. 6. 7. 8.

Performance PALS GMA Agreeableness Conscientiousness Emotional stability Extraversion Openness to experience

M

SD

4.01 4.09 24.41 4.10 3.98 3.71 3.85 3.91

.53 .54 .72 .71 .71 .74 .75 .71

1

2

3

4

5

6

7

8

– .09 .24** .45** .24** .31** .38**

– −.01 −.14 .09 −.22** .10

– .28** .18* .20* .19*

– .30** .08 .03

– −.02 .04

– .33**

. –

– .21** .17* .12 .20* .07 −.18* −.05

Note. n = 133. Significance levels reflect one-tailed tests. *p b .05, **p b .01.

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Table 4 Regression of performance on PALS, GMA, and personality. Stage 1

Stage 2

Predictor

β

β

GMA Agreeableness Conscientiousness Emotional stability Extraversion Openness to experience PALS R2 F ΔR2 FΔ

.16* .11 .21* −.03 −.18* −.03 – .11 2.66** – –

.12 .11 .11 −.05 −.23** −.09 .24* .15 3.09** .04 5.10*

Note. n = 133. Significance levels reflect one-tailed tests. *p b .05, **p b .01.

Hypothesis 1, which proposed that PALS would be positively related to job performance, was supported. The beta was .24 employee performance (p b .05). The research question asked whether PALS would be a stronger predictor of performance than GMA and the five individual personality dimensions. PALS was a stronger predictor than GMA, agreeableness, emotional stability, conscientiousness, and openness to experience. PALS was a significant predictor of performance in stage 2, while GMA, agreeableness, conscientiousness, emotional stability, and openness to experience were not. As noted above, the beta for GMA in stage 2 was .12 (p N .05), and the conscientiousness beta in stage 2 was .11(p N .05). Further, the agreeableness beta was .11 (p N .05), the emotional stability beta was −.05 (p N .05), and the openness to experience beta was −.09 (p N .05). The effect size was extraversion was significant (β = −.23, p b .01). To determine whether the effect size for extraversion was significantly different than the effect size for PALS, a twotailed t-test for the equality of the absolute value of regression coefficients was examined (Paternoster et al., 1998). The effect sizes for PALS and extraversion were not significantly different (t = .12, p N .05). It should be noted that extraversion was negatively related to performance, running counter to previous research (Mount et al., 1998). 4.2.1. Post hoc analysis Similar to study 2, follow-up tests of mediation were conducted to assess the extent to which PALS mediated the GMA– performance and conscientiousness–performance relationships, as both GMA and conscientiousness became non-significant with the inclusion of PALS in the regression equation. Again, the mediating relationships were examined using Preacher and Hayes' (2004) bootstrap approach. The results do not provide evidence for PALS mediating either the GMA–performance and conscientiousness–performance relationships. The relationship between GMA and PALS (b = .01, p N .05) was non-significant, thus precluding assessing the m → y controlling for x and x → y controlling for m relationships. In addition, the bootstrapped indirect effect (k = 5000 resamples) was non-significant, as the 95% confidence interval generated from the sampling distribution did include zero (LL = .00, UL = .01). The relationship between conscientiousness and PALS (b = .34, p b .01) was significant, but the relationship between PALS and performance was non-significant after controlling for conscientiousness (b = .15, p N .05), thus precluding assessing the x → y controlling for m relationship. The bootstrapped indirect effect (k = 5000 resamples) was non-significant, as the 95% confidence interval included zero (LL = −.01, UL = .12). 5. Discussion The purpose of this research was to develop a measure of an individual's self-efficacy towards learning and problem solving— the perceived ability to learn and solve problems (PALS) and examine its relationship with job performance. This effort contributes to the staffing literature by addressing the need to develop new predictors that can help us more fully explain the performance criterion (Cascio & Aguinis, 2008). Our results demonstrate that PALS is a significant predictor of performance for both managers and entry-level employees. Furthermore, PALS accounted for additional explanation in performance beyond other individual differences that have traditionally been used in studies of predictors of performance. In the managerial sample, PALS explained additional variance beyond GMA, GSE, openness to experience, and learning orientation. In the entry-level employee sample, PALS explained additional variance beyond the Big Five personality dimensions—agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience. These findings are noteworthy, following that task-specific self-efficacy (TSSE) beliefs did not significantly predict performance when other key individual differences were considered in Judge et al.'s (2007) research. In the present study, however, PALS was a significant predictor and did explain additional variance in performance. On the whole, the results of this study support our perspective that using a predictor that focuses specifically on confidence relating to learning and problem solving is important given the relevance of learning and problem solving in job performance contexts. Another goal of this study was to demonstrate that PALS is distinct from related constructs such as GSE, openness to experience, and learning orientation. Although GSE, openness to experience, and learning orientation relate to confidence and learning and

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problem solving to varying degrees, PALS is specifically and narrowly focused on this content domain. The confirmatory factor analyses demonstrated that PALS is in fact distinct, although related to, GSE, openness to experience, and learning orientation. Furthermore, PALS was a stronger predictor of performance than these similar constructs, as demonstrated in the sample of managers. Previous research has shown that GMA and conscientiousness are two primary individual differences that consistently predict performance (Hunter & Hunter, 1984; Hurtz & Donovan, 2000). The importance of PALS for predicting performance was further supported by the findings that GMA and conscientiousness became non-significant when PALS was included as a predictor of performance. In the managerial sample, GMA became non-significant with the inclusion of PALS. (Conscientiousness was not assessed with this sample.) In the entry-level employee sample, GMA and conscientiousness both became non-significant with the inclusion of PALS. GMA and conscientiousness may have become non-significant with the inclusion of PALS because PALS may have been a mediator in the GMA–performance and conscientiousness–performance relationships. However, follow-up tests of mediation suggest that PALS does not mediate these relationships. One reason why a mediating relationship was not found in the GMA–performance relationship is that PALS was not strongly related to GMA. The modest association between these constructs may be because GMA is relatively fixed, whereas individuals' perceptions of their abilities are more malleable and subject to situational influences and experience over time. Banudra (1986) suggests that verbal persuasion from others, mastery experiences, and positive feedback can increase an individual's confidence relating to executing a specific task, thus making self-efficacy less dependent on a fixed ability. A possible reason PALS did not mediate the conscientiousness– performance relationship is that they are both motivational constructs, having no casual relationship. While no support for mediation was found, PALS may have reduced the impact of conscientiousness because PALS focuses more narrowly on learning and problem solving, whereas conscientiousness is a more general personality trait. Research is needed to replicate our results in different samples and settings to further establish the predictive validity of PALS relative to GMA and conscientiousness, given the well-established importance of these individual differences in a performance context. In both samples, the overall variance explained by PALS and the other individual differences was modest. One explanation for this finding is possible range restriction in the independent variables because this study used a concurrent validation design (Pedhazur & Schmelkin, 1991). That is, data on the predictors and criteria were collected from job incumbents. The organizations did not use the measures employed in this study to select current job incumbents. However, their selection protocols could have focused on similar constructs, thus creating range restriction. A second explanation is that situational characteristics were not examined, such as organizational climate. Tett and Burnett (2003) argue that situational characteristics can amplify or minimize the extent to which individual differences are expressed and therefore predictive of performance. In the context of PALS, future research should examine its interactive effects with training climate (Tracey & Tews, 2005). The PALS–job performance relationship may be stronger in a work environment that supports ongoing learning and problem solving compared to an unsupportive environment. 5.1. Implications for practice One practical implication is that PALS could be used to make better selection decisions. Moreover, since GMA became nonsignificant in both samples once PALS was used to predict performance, PALS could potentially be used as a substitute for standardized GMA assessments. The often discussed challenge of GMA assessments is that practitioners may be resistant to using them due to the apparent lack of face validity of the numeric, verbal, and logical problems that typify these assessments (Chan, 1997). Situational judgment tests have been suggested as one viable alternative to GMA assessments, given their relationship with performance and GMA (McDaniel, Morgeson, Finnegan, Campion, and Braverman, 2001). Our results suggest that PALS may also potentially be a viable alternative to explicit GMA assessments. Additional research is necessary, however, before firm recommendations can be made for using PALS as a substitute for a standardized GMA assessment given the preliminary nature of the findings from this study. In addition to using PALS for selection purposes, managers should focus on means to further promote individuals' confidence in their ability to learn and solve problems. Just as self-efficacy in general is affected by environmental factors in addition to dispositional influences (Gist & Mitchell, 1992), PALS in particular also may be influenced by environmental factors. For example, self-efficacy beliefs may be enhanced through persuasion, feedback, modeling, and mastery experiences (Gist & Mitchell, 1992). As such, managers should encourage employees' learning and problem-solving efforts, provide effective feedback on the process, appropriately model learning and problem solving, and provide opportunities for individuals to succeed in learning and problemsolving efforts. 5.2. Study limitations and future research opportunities The findings from this research should be interpreted in the context of two primary limitations. The first limitation is that PALS was only examined using a limited set of performance criteria, i.e., dimensions of task performance. Additional work should examine the impact of PALS on contextual dimensions of performance, such as helping behavior. For example, those higher in PALS may be more willing and able to train coworkers and otherwise aid them in their on-the-job efforts. The second study limitation is that only two samples in one industry were used. As such, it is worthwhile to examine the PALS–performance relationship using other types of employees (e.g., knowledge workers) to enhance the generalizability of the current results.

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Several additional opportunities for further research are worth pursuing. One, it is important to examine the mediating mechanisms through which PALS influences performance. For example, what specific learning behaviors do those higher in PALS engage in and how do these behaviors, in turn, impact employee performance? Two, it is worth examining the temporal stability of PALS. That is, to what extent are individuals' perceptions of themselves regarding learning and problem solving relatively stable over time, or do they substantially fluctuate? The degree of temporal stability will help determine whether the application of PALS should be more in a selection context or via managerial interventions once employees are on the job. Three, research should examine the extent to which PALS has disparate impact. That is, to what extent are there subgroup differences within protected classes? In particular, are there racial/ethnic differences in PALS that have been controversial with respect to GMA (Roth, Bevier, Bobko, Switzer, & Tyler, 2006)? Such an assessment was not possible in the present context due to the lack of diversity in the samples. The prediction of employee performance is one of the primary goals of research applied to the workplace. The findings from this study, although preliminary, suggest that PALS may be an important addition to the set of individual differences used to predict performance. As learning and problem solving continue to become more central in today's organizations, researching predictors such as PALS should continue to be a focus of concerted attention. Appendix. PALS, GSE, openness to experience, and learning orientation scales PALS 1. 2. 3. 4. 5. 6.

I can readily find solutions to problems. I can quickly be trained to do almost anything. I have excellent reasoning ability. I retain information with little effort. I quickly learn new things. I quickly understand new situations.

GSE 1. 2. 3. 4. 5. 6. 7. 8.

I am strong enough to overcome life's struggles. At root, I am a weak person (reverse coded). I can handle the situations that life brings. I usually feel that I am an unsuccessful person (reverse coded). I often feel that there is nothing that I can do well (reverse coded). I feel competent to deal effectively with the real world. I often feel like a failure (reverse coded). I usually feel that I can handle the typical problems that come up in life.

Openness to experience 1. 2. 3. 4.

I have a vivid imagination. I am not interested in abstract ideas (reverse coded). I have difficulty understanding abstract ideas (reverse coded). I do not have a good imagination (reverse coded).

Learning orientation 1. 2. 3. 4. 5.

I like challenging work assignments I can learn a lot from. I often look for opportunities to develop new knowledge and skills. I enjoy challenging and difficult tasks where I'll learn new skills. It's important for me to take risks to develop my work abilities. I prefer to work in situations that require a high level of ability and talent.

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